The Navon Task as a Measure of Attention Resolution Efficiency in Children and Adults
Bibliographic record
Abstract
Developmental differences in visual attention may impact reading ability. Our quasi-experimental study investigated these variables in 15 children ages 5–6 years, 47 children ages 7–10 years, and 47 adults. Participants completed a computerized version of the Navon task and the Word Identification subtest of the Woodcock Reading Mastery Test, 3rd ed. In the Navon task, a large letter, composed of smaller letters, was displayed. Participants identified a target letter (H or O) that was presented at either the global (the large letter) or the local level (smaller letters that composed the large letter). There were 16 practice trials and 64 experimental trials (32 target present, with 16 global and 16 local; 32 target absent). Response time was the dependent variable and the Navon level (global vs. local) and age were the independent variables. The Navon task has typically been used to measure precedence, which is the tendency to identify a target fastest at either the global or local level. However, we used the Navon task as a novel measure of attention resolution (AR) efficiency. An absolute timing difference was calculated using the average response time during trials with targets presented at the global or local levels. Smaller absolute timing differences between the globally and locally presented targets indicated more efficient AR modulation, while larger absolute timing differences indicated less-efficient AR modulation. As expected, AR efficiency improved as age increased, partially replicating past AR research. This suggests that absolute timing differences on the Navon task may validly measure AR efficiency. Reading ability was negatively correlated with AR efficiency, indicating that higher reading scores were associated with more efficient AR. These findings suggest that AR efficiency develops during childhood and reaches adult levels by age 7–10. Additionally, AR efficiency may be a marker of reading ability.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".